Artificial immune system for solving constrained optimization problems
In this paper, we present an artifi cial immune system (AIS) based on the CLONALG algorithm for solving constrained (numerical) optimization problems. We develop a new mutation operator which produces large and small step sizes and which aims to provide better exploration capabilities. We validate o...
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| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2006
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/22628 |
| Aporte de: |
| Sumario: | In this paper, we present an artifi cial immune system (AIS) based on the CLONALG algorithm for solving constrained (numerical) optimization problems. We develop a new mutation operator which produces large and small step sizes and which aims to provide better exploration capabilities. We validate our proposed approach with 13 test functions taken from the specialized literature and we compare our results with respect to Stochastic Ranking (which is an approach representative of the state-of-the-art in the area) and with respect to an AIS previously proposed by one of the co-authors |
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